Step-selection analysis (SSA) is a popular tool used by ecologists to define a movement model based on an animal's locomotive capabilities and selection for spatiotemporally explicit variables. The magnitudes and directions of the estimated model parameters provide inference regarding an individual's or species' resource selection, and the resulting movement model can be simulated to infer space use patterns. Despite the popularity of SSA, many studies focus exclusively on interpreting parameter values without estimating the resulting spatial patterns. We advocate here for the full predictive functionality of step-selection analysis and provide methods to use a fitted step-selection function to estimate an animal's space use. Specifically, we use fundamental mathematical principles to construct a transition matrix from the step selection movement process. We demonstrate that transition matrices can efficiently produce the emergent spatial pattern from SSA using two different methods - eigen decomposition and iterations - alleviating the need for thousands of simulations of the underlying model. We show how such matrices may be parameterized under competing definitions of the underlying movement kernel and link these back to realistic ecological processes. To emphasize the flexibility of this approach, we apply these transition matrices to field data from two species with markedly different movement capacities and strategies: wild pigs (Sus scrofa) and long-tailed tits (Aegithalos caudatus). We discuss the caveats in transition matrix parameterization and resulting predictions of space use with respect to the underlying movement process and provide suggestions for the application of this procedure to SSA. Our framework represents a robust tool for space-use prediction from SSA while also highlighting critical gaps in our current knowledge about, and tools to describe, animal space-use processes. In particular, we discuss higher-order Markovian processes and spatiotemporally dynamic variables as particularly important topics for further development.